Enhanced Sweep Efficiency by Use of Smart Water in Tight Oil Reservoirs
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 185032, “Enhance Microscopic Sweep Efficiency by Use of Smart Water in Tight and Very Tight Oil Reservoirs,” by T. Kadeethum, H.K. Sarma, and B.B. Maini, University of Calgary, prepared for the 2017 SPE Canada Unconventional Resources Conference, Calgary, 15–16 February. The paper has not been peer reviewed. In the literature, improvement of oil recovery in smart-water-injection schemes has been shown to be mediated by wettability alteration. This process reduces residual oil saturation, which, in turn, affects microscopic sweep efficiency and leads to subsequent enhancement of overall waterflood performance. Currently, there are few studies on smart waterflooding in tight and very tight oil reservoirs. This work examines smart-waterflood opportunities in such reservoirs. Introduction Residual-oil-saturation reduction improves microscopic sweep efficiency and, therefore, overall waterflood performance. Furthermore, decreasing endpoint water relative permeability diminishes mobility of the water phase such that water production is similarly reduced. Though these circumstances improve oil-production behavior, the primary parameters that lead to this improvement are still not well-understood. Among the statistically significant parameters that can influence smart-waterflood performance is clay content. One plausible explanation for the strong correlation between clay content and oil recovery is the positive correlation between cation-exchange capacity (CEC) and clay content. With higher CEC values, more rock surface may be charged. This results in either the expansion or compression of the double layer, which also induces a wettability alteration. Although tight oil reservoirs have limited flow capability, high CEC values in these reservoirs facilitate the wettability-alteration process. The objectives of the complete paper are to ex-amine the smart-waterflood potential in tight and very tight oil reservoirs, and to identify the CEC effect on smart-water-injection performance. The complete paper provides a discussion of the methodology (procedures and strategies) of the study. Discussion The literature contains evidence of smart-waterflood performance, with some works demonstrating that smart water improves oil recovery by reducing residual oil saturation. Furthermore, it decreases the endpoint water relative permeability. Smart water also improves microscopic sweep efficiency, leading to overall waterflooding efficiency. There is a distinct effect of porosity mean and porosity variance on CEC; there is furthermore a profound effect of CEC upon smart-water performance. Simulation properties relevant to the CEC effect are shown in Table 3 of the complete paper. There are two main types of fluid composition: in-situ fluid composition, which is the initial fluid in the reservoir at timestep zero, and the injected-fluid composition, which is the fluid that is forced into the reservoir at timestep greater than zero. Three homogeneous-reservoir cases with varying CEC values are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".